A Simple and Easy Approach for Home Appliances Energy Consumption Prediction in Residential Buildings Using Machine Learning Techniques

The accurate analysis of energy consumption by home appliances for future energy management in residential buildings is a challenging problem due to its high impact on the human surrounding environment. In this paper, a prediction methodology is presented for energy consumption of home appliances in residential buildings. The aim of the paper is the daily power consumption prediction of home appliances based on classification according to the hourly consumed power of all home appliances being used in residential buildings. The process consists of five stages: data source, data collection, feature extraction, prediction, and performance evaluation. Different machine learning algorithms have been applied to data containing historical hourly energy consumption of home appliances used in residential buildings. We have divided data into different training and testing ratios and have applied different quantitative and qualitative measures for finding the prediction capability and efficiency of each algorithm. After performing extensive experiments, it has been concluded that the highest accuracy of 98.07% has been observed for Logistic Regression for 70-30% training, and testing ratio. The Multi-Layer Perceptron and Random Forest have achieved 96.53%, 96.15% accuracies for 75-25%, training, and testing ratios. The accuracy of KNN was 94.96% with 60-40% training, and testing ratios. For finding the further effectiveness of the proposed model, crossvalidation with different folds have been applied. Each classifier also shows significant variations in the performance with different ratios of training and testing proportions.

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